Deleting parameter learning folder

There was an extra folder with the Matlab/Octave parameter learning files. These were out of date, so I deleted them and kept the versions in the sourcecoades directory.

1 files changed, 0 insertions, 107 deletions

diff --git a/BNW_parameter_learning/Predictmultipleintervention.m b/BNW_parameter_learning/Predictmultipleintervention.m
deleted file mode 100644
index 7e6140a9..00000000
--- a/BNW_parameter_learning/Predictmultipleintervention.m
+++ /dev/null
@@ -1,107 +0,0 @@
-function Predictmultipleintervention(pre)
-% Predictmultipleintervention is used when predicting the impact of
-%    intervention on the network. The 'multiple' part refers to 
-%    it working when intervention for multiple nodes is entered.
-%
-% The input is 'pre'-- the prefix for the network and data
-%      in BNW. It reads information from several files from BNW. 
-%
-% The output is ???net_figure_new.txt. It also calls 
-%      writeParameters_int to write the parameter file.
-%
-% It is called by the run_octave_inv file in the 'sourcecodes' directory.
-
-dfile=strcat(pre,'structure_input.txt');
-sfile=dfile;
-dfile=strcat(pre,'continuous_input.txt');
-nnodefile=strcat(pre,'nnode.txt');
-
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
-fvarnamefile=strcat(pre,'varname.txt');
-
-varfile = fopen(fvarnamefile,'r');
-
-Std_flag=true;
-[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
-
-[bnet]=parameterLearning(bnet,cases);
-
-fvarfile=strcat(pre,'var.txt');
-fvar = fopen(fvarfile,'r');                           
-select_var_new = fscanf(fvar,'%d');
-
-nm = numel(select_var_new);
-
-varlabels = cell(1,nm);
-varbuffer = fgetl(varfile);    %get header line as a string
-for j=1:nm
-    [varnext,varbuffer] = strtok(varbuffer);
-    varlabels{j} = varnext;
-    for i=1:nnodes    
-        if strcmp(varlabels{j},labels{i})
-            select_var_new(j)=i;
-        end
-     end    
-    
-end
-
-
-
-
-fvardfile=strcat(pre,'vardata.txt');
-
-fvard = fopen(fvardfile,'r');
-
-select_var_data_new = fscanf(fvard,'%f');
-
-means_orig = cell(1,nnodes);
-stdevs_orig = cell(1,nnodes);
-labels_orig = cell(1,nnodes);
-%Read in original means and standard deviations
-mapfile = strcat(pre,'map.txt');
-fmap = fopen(mapfile,'r');
-for i=1:nnodes
-    buffer = fgetl(mapfile);
-    temp = cell(1,3);
-    for j=1:3
-        [next,buffer] = strtok(buffer);
-        temp{j} = next;
-    end
-    labels_orig{i} = temp{1};
-    means_orig{i} = str2num(temp{3});
-    stdevs_orig{i} = str2num(temp{2});
-end
-fclose(fmap);
-
-%Need to map the means and stdevs to the correct labels
-means = cell(1,nnodes);
-stdevs = cell(1,nnodes);
-%Read in labels in new order.
-labelsnew = cell(1,nnodes);
-mapdatafile = strcat(pre,'mapdata.txt');
-fmapdata = fopen(mapdatafile,'r');
-buffer = fgetl(fmapdata);
-for i = 1:nnodes
-    [next,buffer ] = strtok(buffer);
-    labelsnew{i} = next;
-end
-fclose(fmapdata);
-for i = 1:nnodes
-    for j = 1:nnodes
-       if strcmp(labelsnew{i},labels_orig{j})
-          means{i} = means_orig{j};
-          stdevs{i} = stdevs_orig{j};
-          break
-       end
-    end
-end
-
-filename=strcat(pre,'net_figure_new.txt');
-
-drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new);
-
-writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new);
-
-end